Machine learning-driven nanoparticle toxicity

IF 6.2 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Zied Hosni , Sofiene Achour , Fatma Saadi , Yangfan Chen , Mohammed Al Qaraghuli
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引用次数: 0

Abstract

This study presents a comprehensive machine learning-driven analysis to understand and predict the toxicity of nanoparticles (NPs), a crucial aspect in ensuring the safe application of nanotechnology in medicine, pharmaceuticals, biotechnology, and various other industries. By using a robust dataset, we deployed Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms to identify key NP features that significantly influence cellular toxicity. The integration of Shapley Additive exPlanations (SHAP) values provided an interpretative insight into the predictive models, allowing for a quantitative assessment of feature impact. Our findings highlighted the inverse relationship between NP concentration and cell viability and the heightened toxicity of smaller NPs due to their larger surface-to-volume ratios. Notably, the LightGBM model's sensitivity to zeta potential elucidates the nuanced impact of surface charge on cytotoxic effects. The results from this investigation can guide the synthesis of safer NPs, emphasized the need to consider these critical features to mitigate toxicity while maintaining functional integrity. The study underlines the complexity of NP toxicity modeling and the necessity for advanced analytical methods to capture the multifaceted nature of nanomaterial interactions with biological systems. This work lays the groundwork for future research aimed at refining NP design for safer biomedical applications and consumer products, marking a significant step towards responsible nanotechnology development.
机器学习驱动的纳米颗粒毒性
这项研究提出了一个全面的机器学习驱动的分析,以了解和预测纳米粒子(NPs)的毒性,这是确保纳米技术在医学,制药,生物技术和各种其他行业安全应用的关键方面。通过使用稳健的数据集,我们部署了随机森林(RF)和光梯度增强机(LightGBM)算法来识别显著影响细胞毒性的关键NP特征。Shapley加性解释(SHAP)值的集成为预测模型提供了解释性的见解,允许对特征影响进行定量评估。我们的研究结果强调了NP浓度与细胞活力之间的反比关系,以及较小的NP由于其较大的表面体积比而增加的毒性。值得注意的是,LightGBM模型对zeta电位的敏感性阐明了表面电荷对细胞毒性作用的细微影响。这项研究的结果可以指导更安全的NPs的合成,强调需要考虑这些关键特征,以减轻毒性,同时保持功能完整性。该研究强调了NP毒性建模的复杂性和先进分析方法的必要性,以捕捉纳米材料与生物系统相互作用的多面性。这项工作为未来的研究奠定了基础,旨在为更安全的生物医学应用和消费产品改进NP设计,标志着向负责任的纳米技术发展迈出了重要的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.10
自引率
5.90%
发文量
1234
审稿时长
88 days
期刊介绍: Ecotoxicology and Environmental Safety is a multi-disciplinary journal that focuses on understanding the exposure and effects of environmental contamination on organisms including human health. The scope of the journal covers three main themes. The topics within these themes, indicated below, include (but are not limited to) the following: Ecotoxicology、Environmental Chemistry、Environmental Safety etc.
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